Overview

Dataset statistics

Number of variables19
Number of observations2059
Missing cells521
Missing cells (%)1.3%
Duplicate rows8
Duplicate rows (%)0.4%
Total size in memory307.8 KiB
Average record size in memory153.1 B

Variable types

Categorical6
Numeric13

Alerts

Dataset has 8 (0.4%) duplicate rowsDuplicates
Brand is highly overall correlated with Engine and 6 other fieldsHigh correlation
Car_age is highly overall correlated with Kilometer and 2 other fieldsHigh correlation
Drivetrain is highly overall correlated with Engine and 5 other fieldsHigh correlation
Engine is highly overall correlated with Brand and 11 other fieldsHigh correlation
Fuel Tank Capacity is highly overall correlated with Drivetrain and 9 other fieldsHigh correlation
Height is highly overall correlated with MakeHigh correlation
Kilometer is highly overall correlated with Car_age and 1 other fieldsHigh correlation
Length is highly overall correlated with Brand and 7 other fieldsHigh correlation
Make is highly overall correlated with Brand and 7 other fieldsHigh correlation
Max Power bhp is highly overall correlated with Brand and 10 other fieldsHigh correlation
Max Power rpm is highly overall correlated with Engine and 4 other fieldsHigh correlation
Max Torque Nm is highly overall correlated with Brand and 11 other fieldsHigh correlation
Max Torque Rpm is highly overall correlated with Engine and 6 other fieldsHigh correlation
Price is highly overall correlated with Car_age and 9 other fieldsHigh correlation
Transmission is highly overall correlated with Brand and 7 other fieldsHigh correlation
Width is highly overall correlated with Brand and 10 other fieldsHigh correlation
Year is highly overall correlated with Car_age and 2 other fieldsHigh correlation
Fuel Type is highly imbalanced (61.5%)Imbalance
Owner is highly imbalanced (64.4%)Imbalance
Engine has 80 (3.9%) missing valuesMissing
Drivetrain has 136 (6.6%) missing valuesMissing
Length has 64 (3.1%) missing valuesMissing
Width has 64 (3.1%) missing valuesMissing
Height has 64 (3.1%) missing valuesMissing
Fuel Tank Capacity has 113 (5.5%) missing valuesMissing
Kilometer is highly skewed (γ1 = 20.98071903)Skewed

Reproduction

Analysis started2026-02-14 08:06:42.364994
Analysis finished2026-02-14 08:07:14.125927
Duration31.76 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Make
Categorical

High correlation 

Distinct33
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Maruti Suzuki
440 
Hyundai
349 
Mercedes-Benz
171 
Honda
158 
Toyota
132 
Other values (28)
809 

Length

Max length13
Median length10
Mean length7.9791161
Min length2

Characters and Unicode

Total characters16429
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowHonda
2nd rowMaruti Suzuki
3rd rowHyundai
4th rowToyota
5th rowToyota

Common Values

ValueCountFrequency (%)
Maruti Suzuki440
21.4%
Hyundai349
16.9%
Mercedes-Benz171
 
8.3%
Honda158
 
7.7%
Toyota132
 
6.4%
Audi127
 
6.2%
BMW121
 
5.9%
Mahindra119
 
5.8%
Tata57
 
2.8%
Volkswagen50
 
2.4%
Other values (23)335
16.3%

Length

2026-02-14T13:37:14.242280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti440
17.4%
suzuki440
17.4%
hyundai349
13.8%
mercedes-benz171
 
6.8%
honda158
 
6.2%
toyota132
 
5.2%
audi127
 
5.0%
bmw121
 
4.8%
mahindra119
 
4.7%
tata57
 
2.3%
Other values (25)418
16.5%

Most occurring characters

ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Price
Real number (ℝ)

High correlation 

Distinct619
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1702991.7
Minimum49000
Maximum35000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:14.394061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49000
5-th percentile250000
Q1484999
median825000
Q31925000
95-th percentile5900000
Maximum35000000
Range34951000
Interquartile range (IQR)1440001

Descriptive statistics

Standard deviation2419880.6
Coefficient of variation (CV)1.4209586
Kurtosis40.298155
Mean1702991.7
Median Absolute Deviation (MAD)465000
Skewness4.9651429
Sum3.5064599 × 109
Variance5.8558223 × 1012
MonotonicityNot monotonic
2026-02-14T13:37:14.560680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42500026
 
1.3%
62500024
 
1.2%
65000022
 
1.1%
37500020
 
1.0%
45000020
 
1.0%
25000019
 
0.9%
55000019
 
0.9%
67500019
 
0.9%
32500018
 
0.9%
72500016
 
0.8%
Other values (609)1856
90.1%
ValueCountFrequency (%)
490001
 
< 0.1%
710011
 
< 0.1%
1000001
 
< 0.1%
1149991
 
< 0.1%
1200002
0.1%
1300002
0.1%
1350001
 
< 0.1%
1400001
 
< 0.1%
1410001
 
< 0.1%
1450003
0.1%
ValueCountFrequency (%)
350000001
 
< 0.1%
275000001
 
< 0.1%
240000001
 
< 0.1%
220000001
 
< 0.1%
200000003
0.1%
193000001
 
< 0.1%
185000002
0.1%
180000001
 
< 0.1%
162000001
 
< 0.1%
149000001
 
< 0.1%

Year
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.4254
Minimum1988
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:14.701705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile2011
Q12014
median2017
Q32019
95-th percentile2021
Maximum2022
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3635636
Coefficient of variation (CV)0.0016680823
Kurtosis2.9266435
Mean2016.4254
Median Absolute Deviation (MAD)2
Skewness-0.84068453
Sum4151820
Variance11.31356
MonotonicityNot monotonic
2026-02-14T13:37:14.855438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2018268
13.0%
2017262
12.7%
2019218
10.6%
2014192
9.3%
2016187
9.1%
2015178
8.6%
2021156
7.6%
2020132
6.4%
2013128
6.2%
201292
 
4.5%
Other values (12)246
11.9%
ValueCountFrequency (%)
19881
 
< 0.1%
19961
 
< 0.1%
20001
 
< 0.1%
20021
 
< 0.1%
20041
 
< 0.1%
20062
 
0.1%
20076
 
0.3%
200813
 
0.6%
200933
1.6%
201027
1.3%
ValueCountFrequency (%)
202281
 
3.9%
2021156
7.6%
2020132
6.4%
2019218
10.6%
2018268
13.0%
2017262
12.7%
2016187
9.1%
2015178
8.6%
2014192
9.3%
2013128
6.2%

Kilometer
Real number (ℝ)

High correlation  Skewed 

Distinct847
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54224.714
Minimum0
Maximum2000000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:15.112459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8000
Q129000
median50000
Q372000
95-th percentile107991.1
Maximum2000000
Range2000000
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation57361.721
Coefficient of variation (CV)1.057852
Kurtosis669.60863
Mean54224.714
Median Absolute Deviation (MAD)22000
Skewness20.980719
Sum1.1164869 × 108
Variance3.2903671 × 109
MonotonicityNot monotonic
2026-02-14T13:37:15.457542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6500033
 
1.6%
7200032
 
1.6%
4500029
 
1.4%
5000029
 
1.4%
7500029
 
1.4%
4200028
 
1.4%
7000026
 
1.3%
5500026
 
1.3%
3500022
 
1.1%
7800021
 
1.0%
Other values (837)1784
86.6%
ValueCountFrequency (%)
01
< 0.1%
12
0.1%
751
< 0.1%
5001
< 0.1%
6001
< 0.1%
10001
< 0.1%
11021
< 0.1%
13002
0.1%
13741
< 0.1%
15001
< 0.1%
ValueCountFrequency (%)
20000001
< 0.1%
9250001
< 0.1%
4400001
< 0.1%
2612361
< 0.1%
2400001
< 0.1%
2220001
< 0.1%
2190001
< 0.1%
2110001
< 0.1%
1950001
< 0.1%
1923261
< 0.1%

Fuel Type
Categorical

Imbalance 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Diesel
1049 
Petrol
942 
CNG
 
50
Electric
 
7
LPG
 
5
Other values (4)
 
6

Length

Max length12
Median length6
Mean length5.9339485
Min length3

Characters and Unicode

Total characters12218
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowPetrol
2nd rowDiesel
3rd rowPetrol
4th rowPetrol
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel1049
50.9%
Petrol942
45.8%
CNG50
 
2.4%
Electric7
 
0.3%
LPG5
 
0.2%
Hybrid3
 
0.1%
CNG + CNG1
 
< 0.1%
Petrol + CNG1
 
< 0.1%
Petrol + LPG1
 
< 0.1%

Length

2026-02-14T13:37:15.616053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T13:37:15.752581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel1049
50.8%
petrol944
45.7%
cng53
 
2.6%
electric7
 
0.3%
lpg6
 
0.3%
hybrid3
 
0.1%
3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Transmission
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Manual
1133 
Automatic
926 

Length

Max length9
Median length6
Mean length7.3491986
Min length6

Characters and Unicode

Total characters15132
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual1133
55.0%
Automatic926
45.0%

Length

2026-02-14T13:37:15.910051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T13:37:15.993143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual1133
55.0%
automatic926
45.0%

Most occurring characters

ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
n1133
 
7.5%
M1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
n1133
 
7.5%
M1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
n1133
 
7.5%
M1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
n1133
 
7.5%
M1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Owner
Categorical

Imbalance 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
First
1619 
Second
373 
Third
 
42
UnRegistered Car
 
21
Fourth
 
3

Length

Max length16
Median length5
Mean length5.296746
Min length5

Characters and Unicode

Total characters10906
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFirst
2nd rowSecond
3rd rowFirst
4th rowFirst
5th rowFirst

Common Values

ValueCountFrequency (%)
First1619
78.6%
Second373
 
18.1%
Third42
 
2.0%
UnRegistered Car21
 
1.0%
Fourth3
 
0.1%
4 or More1
 
< 0.1%

Length

2026-02-14T13:37:16.093187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T13:37:16.193122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first1619
77.8%
second373
 
17.9%
third42
 
2.0%
unregistered21
 
1.0%
car21
 
1.0%
fourth3
 
0.1%
41
 
< 0.1%
or1
 
< 0.1%
more1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Engine
Real number (ℝ)

High correlation  Missing 

Distinct108
Distinct (%)5.5%
Missing80
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1692.5755
Minimum624
Maximum6592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2026-02-14T13:37:16.356106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile998
Q11197
median1498
Q31995
95-th percentile2987
Maximum6592
Range5968
Interquartile range (IQR)798

Descriptive statistics

Standard deviation643.73629
Coefficient of variation (CV)0.38032943
Kurtosis6.7740649
Mean1692.5755
Median Absolute Deviation (MAD)412
Skewness1.7550597
Sum3349607
Variance414396.42
MonotonicityNot monotonic
2026-02-14T13:37:16.668453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1197231
 
11.2%
1248122
 
5.9%
998121
 
5.9%
149784
 
4.1%
199582
 
4.0%
196882
 
4.0%
217973
 
3.5%
149864
 
3.1%
158256
 
2.7%
214351
 
2.5%
Other values (98)1013
49.2%
(Missing)80
 
3.9%
ValueCountFrequency (%)
6241
 
< 0.1%
7931
 
< 0.1%
79630
 
1.5%
79910
 
0.5%
81413
 
0.6%
9361
 
< 0.1%
9951
 
< 0.1%
998121
5.9%
99931
 
1.5%
10472
 
0.1%
ValueCountFrequency (%)
65923
0.1%
54611
 
< 0.1%
52041
 
< 0.1%
49512
0.1%
48061
 
< 0.1%
46632
0.1%
41632
0.1%
39821
 
< 0.1%
39021
 
< 0.1%
34983
0.1%

Drivetrain
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing136
Missing (%)6.6%
Memory size16.2 KiB
FWD
1330 
RWD
321 
AWD
272 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5769
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFWD
2nd rowFWD
3rd rowFWD
4th rowFWD
5th rowRWD

Common Values

ValueCountFrequency (%)
FWD1330
64.6%
RWD321
 
15.6%
AWD272
 
13.2%
(Missing)136
 
6.6%

Length

2026-02-14T13:37:16.944060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T13:37:17.077398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd1330
69.2%
rwd321
 
16.7%
awd272
 
14.1%

Most occurring characters

ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Length
Real number (ℝ)

High correlation  Missing 

Distinct248
Distinct (%)12.4%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean4280.8607
Minimum3099
Maximum5569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:17.267529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3099
5-th percentile3565
Q13985
median4370
Q34629
95-th percentile4936
Maximum5569
Range2470
Interquartile range (IQR)644

Descriptive statistics

Standard deviation442.45851
Coefficient of variation (CV)0.10335737
Kurtosis-0.82045722
Mean4280.8607
Median Absolute Deviation (MAD)375
Skewness-0.02152263
Sum8540317
Variance195769.53
MonotonicityNot monotonic
2026-02-14T13:37:17.509187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3995221
 
10.7%
444070
 
3.4%
427057
 
2.8%
398555
 
2.7%
445644
 
2.1%
385042
 
2.0%
376540
 
1.9%
458539
 
1.9%
449036
 
1.7%
473534
 
1.7%
Other values (238)1357
65.9%
(Missing)64
 
3.1%
ValueCountFrequency (%)
30991
 
< 0.1%
339520
1.0%
34294
 
0.2%
34452
 
0.1%
349523
1.1%
35154
 
0.2%
35204
 
0.2%
353913
0.6%
354511
0.5%
356525
1.2%
ValueCountFrequency (%)
55692
 
0.1%
54621
 
< 0.1%
54531
 
< 0.1%
53991
 
< 0.1%
52651
 
< 0.1%
52551
 
< 0.1%
52523
0.1%
52471
 
< 0.1%
52465
0.2%
52263
0.1%

Width
Real number (ℝ)

High correlation  Missing 

Distinct170
Distinct (%)8.5%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1767.992
Minimum1475
Maximum2220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:17.814851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1475
5-th percentile1495
Q11695
median1770
Q31831.5
95-th percentile2044
Maximum2220
Range745
Interquartile range (IQR)136.5

Descriptive statistics

Standard deviation135.26583
Coefficient of variation (CV)0.076508167
Kurtosis0.89759088
Mean1767.992
Median Absolute Deviation (MAD)75
Skewness0.30833494
Sum3527144
Variance18296.843
MonotonicityNot monotonic
2026-02-14T13:37:18.084239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1695201
 
9.8%
178062
 
3.0%
179061
 
3.0%
166051
 
2.5%
168051
 
2.5%
147549
 
2.4%
189049
 
2.4%
174546
 
2.2%
182046
 
2.2%
173444
 
2.1%
Other values (160)1335
64.8%
(Missing)64
 
3.1%
ValueCountFrequency (%)
147549
2.4%
149031
1.5%
149529
1.4%
15003
 
0.1%
15152
 
0.1%
15206
 
0.3%
15257
 
0.3%
155014
 
0.7%
15604
 
0.2%
157924
1.2%
ValueCountFrequency (%)
22204
 
0.2%
21831
 
< 0.1%
21731
 
< 0.1%
21578
0.4%
21551
 
< 0.1%
214119
0.9%
21391
 
< 0.1%
21209
0.4%
21101
 
< 0.1%
21051
 
< 0.1%

Height
Real number (ℝ)

High correlation  Missing 

Distinct196
Distinct (%)9.8%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1591.7353
Minimum1165
Maximum1995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:18.338920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1165
5-th percentile1432
Q11485
median1545
Q31675
95-th percentile1845.3
Maximum1995
Range830
Interquartile range (IQR)190

Descriptive statistics

Standard deviation136.07396
Coefficient of variation (CV)0.085487802
Kurtosis0.034725652
Mean1591.7353
Median Absolute Deviation (MAD)79
Skewness0.83777993
Sum3175512
Variance18516.121
MonotonicityNot monotonic
2026-02-14T13:37:18.562491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147588
 
4.3%
150580
 
3.9%
153063
 
3.1%
152061
 
3.0%
149558
 
2.8%
164056
 
2.7%
150051
 
2.5%
148546
 
2.2%
155544
 
2.1%
170043
 
2.1%
Other values (186)1405
68.2%
(Missing)64
 
3.1%
ValueCountFrequency (%)
11651
< 0.1%
12131
< 0.1%
12812
0.1%
12951
< 0.1%
12971
< 0.1%
13041
< 0.1%
13531
< 0.1%
13662
0.1%
13701
< 0.1%
13912
0.1%
ValueCountFrequency (%)
199520
1.0%
197510
0.5%
19402
 
0.1%
19309
0.4%
19251
 
< 0.1%
19225
 
0.2%
19202
 
0.1%
19011
 
< 0.1%
19001
 
< 0.1%
18953
 
0.1%

Fuel Tank Capacity
Real number (ℝ)

High correlation  Missing 

Distinct55
Distinct (%)2.8%
Missing113
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean52.00221
Minimum15
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:18.773597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35
Q141.25
median50
Q360
95-th percentile80
Maximum105
Range90
Interquartile range (IQR)18.75

Descriptive statistics

Standard deviation15.110198
Coefficient of variation (CV)0.29056838
Kurtosis0.35516399
Mean52.00221
Median Absolute Deviation (MAD)10
Skewness0.8530031
Sum101196.3
Variance228.31808
MonotonicityNot monotonic
2026-02-14T13:37:18.937032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35222
 
10.8%
60155
 
7.5%
45154
 
7.5%
43146
 
7.1%
50132
 
6.4%
55117
 
5.7%
42111
 
5.4%
40106
 
5.1%
8092
 
4.5%
3789
 
4.3%
Other values (45)622
30.2%
(Missing)113
 
5.5%
ValueCountFrequency (%)
151
 
< 0.1%
275
 
0.2%
2830
 
1.5%
3227
 
1.3%
35222
10.8%
3789
4.3%
381
 
< 0.1%
40106
5.1%
416
 
0.3%
42111
5.4%
ValueCountFrequency (%)
1053
 
0.1%
1041
 
< 0.1%
10015
0.7%
952
 
0.1%
9318
0.9%
921
 
< 0.1%
9010
0.5%
855
 
0.2%
836
 
0.3%
82.53
 
0.1%

Max Power bhp
Real number (ℝ)

High correlation 

Distinct165
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.39339
Minimum35
Maximum660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:19.085469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile67
Q183
median117
Q3169
95-th percentile248
Maximum660
Range625
Interquartile range (IQR)86

Descriptive statistics

Standard deviation63.806016
Coefficient of variation (CV)0.49311648
Kurtosis9.0595894
Mean129.39339
Median Absolute Deviation (MAD)35
Skewness2.1218789
Sum266421
Variance4071.2077
MonotonicityNot monotonic
2026-02-14T13:37:19.236650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89139
 
6.8%
6799
 
4.8%
12481
 
3.9%
8262
 
3.0%
7461
 
3.0%
8355
 
2.7%
12655
 
2.7%
17449
 
2.4%
17748
 
2.3%
18843
 
2.1%
Other values (155)1367
66.4%
ValueCountFrequency (%)
351
 
< 0.1%
392
 
0.1%
463
 
0.1%
4727
1.3%
481
 
< 0.1%
5310
 
0.5%
5511
0.5%
562
 
0.1%
5827
1.3%
595
 
0.2%
ValueCountFrequency (%)
6601
 
< 0.1%
6021
 
< 0.1%
5703
0.1%
5001
 
< 0.1%
4631
 
< 0.1%
4532
0.1%
4442
0.1%
3962
0.1%
3851
 
< 0.1%
3681
 
< 0.1%

Max Power rpm
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4757.3171
Minimum2910
Maximum8250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:19.378569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2910
5-th percentile3500
Q14000
median4313
Q36000
95-th percentile6400
Maximum8250
Range5340
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1048.6871
Coefficient of variation (CV)0.22043665
Kurtosis-1.2810708
Mean4757.3171
Median Absolute Deviation (MAD)713
Skewness0.38719747
Sum9795316
Variance1099744.6
MonotonicityNot monotonic
2026-02-14T13:37:19.514911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4000431
20.9%
6000422
20.5%
4313212
10.3%
3750143
 
6.9%
550097
 
4.7%
360091
 
4.4%
380072
 
3.5%
420067
 
3.3%
340062
 
3.0%
500058
 
2.8%
Other values (29)404
19.6%
ValueCountFrequency (%)
29108
 
0.4%
300016
 
0.8%
320011
 
0.5%
340062
 
3.0%
350047
 
2.3%
360091
 
4.4%
3750143
 
6.9%
380072
 
3.5%
39008
 
0.4%
4000431
20.9%
ValueCountFrequency (%)
82503
 
0.1%
80001
 
< 0.1%
74001
 
< 0.1%
660054
 
2.6%
650014
 
0.7%
640035
 
1.7%
630032
 
1.6%
62504
 
0.2%
620053
 
2.6%
6000422
20.5%

Max Torque Nm
Real number (ℝ)

High correlation 

Distinct138
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.45896
Minimum48
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:19.663214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile90
Q1115
median205
Q3343
95-th percentile500
Maximum780
Range732
Interquartile range (IQR)228

Descriptive statistics

Standard deviation137.7296
Coefficient of variation (CV)0.56111049
Kurtosis0.36031245
Mean245.45896
Median Absolute Deviation (MAD)92
Skewness0.90602853
Sum505400
Variance18969.442
MonotonicityNot monotonic
2026-02-14T13:37:19.824468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200125
 
6.1%
400114
 
5.5%
11584
 
4.1%
23680
 
3.9%
11476
 
3.7%
9073
 
3.5%
25071
 
3.4%
38067
 
3.3%
35064
 
3.1%
19064
 
3.1%
Other values (128)1241
60.3%
ValueCountFrequency (%)
481
 
< 0.1%
542
 
0.1%
626
 
0.3%
6922
1.1%
7210
0.5%
7513
0.6%
7720
1.0%
788
 
0.4%
847
 
0.3%
852
 
0.1%
ValueCountFrequency (%)
7803
 
0.1%
7601
 
< 0.1%
7006
 
0.3%
6891
 
< 0.1%
62017
0.8%
6198
 
0.4%
60022
1.1%
5801
 
< 0.1%
56023
1.1%
5508
 
0.4%

Max Torque Rpm
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2565.086
Minimum150
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:19.989463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile1400
Q11600
median2000
Q34000
95-th percentile4600
Maximum6500
Range6350
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1147.3579
Coefficient of variation (CV)0.44729803
Kurtosis-1.1053088
Mean2565.086
Median Absolute Deviation (MAD)500
Skewness0.67548871
Sum5281512
Variance1316430.1
MonotonicityNot monotonic
2026-02-14T13:37:20.145910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1750382
18.6%
4000240
11.7%
2329208
10.1%
1500202
9.8%
1600160
 
7.8%
3500128
 
6.2%
200091
 
4.4%
140077
 
3.7%
190055
 
2.7%
420048
 
2.3%
Other values (42)468
22.7%
ValueCountFrequency (%)
1501
 
< 0.1%
120030
 
1.5%
125024
 
1.2%
13003
 
0.1%
13402
 
0.1%
13504
 
0.2%
13601
 
< 0.1%
13702
 
0.1%
140077
3.7%
145011
 
0.5%
ValueCountFrequency (%)
65001
 
< 0.1%
56001
 
< 0.1%
50002
 
0.1%
485025
1.2%
480039
1.9%
47003
 
0.1%
460042
2.0%
450047
2.3%
440036
1.7%
438610
 
0.5%

Car_age
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5745508
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2026-02-14T13:37:20.281375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile12
Maximum35
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3635636
Coefficient of variation (CV)0.51160356
Kurtosis2.9266435
Mean6.5745508
Median Absolute Deviation (MAD)2
Skewness0.84068453
Sum13537
Variance11.31356
MonotonicityNot monotonic
2026-02-14T13:37:20.443283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5268
13.0%
6262
12.7%
4218
10.6%
9192
9.3%
7187
9.1%
8178
8.6%
2156
7.6%
3132
6.4%
10128
6.2%
1192
 
4.5%
Other values (12)246
11.9%
ValueCountFrequency (%)
181
 
3.9%
2156
7.6%
3132
6.4%
4218
10.6%
5268
13.0%
6262
12.7%
7187
9.1%
8178
8.6%
9192
9.3%
10128
6.2%
ValueCountFrequency (%)
351
 
< 0.1%
271
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
172
 
0.1%
166
 
0.3%
1513
 
0.6%
1433
1.6%
1327
1.3%

Brand
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Budget Cars
1128 
Luxury cars
531 
Mid-range cars
400 

Length

Max length14
Median length11
Mean length11.840699
Min length11

Characters and Unicode

Total characters24380
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBudget Cars
2nd rowBudget Cars
3rd rowBudget Cars
4th rowBudget Cars
5th rowMid-range cars

Common Values

ValueCountFrequency (%)
Budget Cars1128
54.8%
Luxury cars531
25.8%
Mid-range cars400
 
19.4%

Length

2026-02-14T13:37:20.591712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T13:37:20.680145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cars2059
50.0%
budget1128
27.4%
luxury531
 
12.9%
mid-range400
 
9.7%

Most occurring characters

ValueCountFrequency (%)
r2990
12.3%
2590
10.6%
a2459
10.1%
u2190
9.0%
s2059
 
8.4%
g1528
 
6.3%
d1528
 
6.3%
e1528
 
6.3%
B1128
 
4.6%
t1128
 
4.6%
Other values (9)5252
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)24380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2990
12.3%
2590
10.6%
a2459
10.1%
u2190
9.0%
s2059
 
8.4%
g1528
 
6.3%
d1528
 
6.3%
e1528
 
6.3%
B1128
 
4.6%
t1128
 
4.6%
Other values (9)5252
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2990
12.3%
2590
10.6%
a2459
10.1%
u2190
9.0%
s2059
 
8.4%
g1528
 
6.3%
d1528
 
6.3%
e1528
 
6.3%
B1128
 
4.6%
t1128
 
4.6%
Other values (9)5252
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2990
12.3%
2590
10.6%
a2459
10.1%
u2190
9.0%
s2059
 
8.4%
g1528
 
6.3%
d1528
 
6.3%
e1528
 
6.3%
B1128
 
4.6%
t1128
 
4.6%
Other values (9)5252
21.5%

Interactions

2026-02-14T13:37:11.049047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:44.220808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:46.463968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:49.438085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:52.072898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:54.807809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:57.972862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:59.901284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.005952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.633981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:05.341662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.468121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.298478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:11.186090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:44.407415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:46.623346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:49.648520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:52.216441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:55.033790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.116328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:00.372877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.137274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.766110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:05.486310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.607135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.444521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:11.320579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:44.541343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:47.167426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:49.896736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:52.417597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:55.249324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.254447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:00.514559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.266208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.896295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:05.727801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.741983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.593822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:11.464437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:44.706432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:47.371127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:50.142925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:52.730288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:55.549484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.399169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:00.653119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.403858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.034932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:05.909705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.882188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.749589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:11.606658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:44.835588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:47.542171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:50.383166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:53.222036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:55.864736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.533629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:00.782320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.525735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.164193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:06.034500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.013340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.884317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:11.746788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:44.987256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:47.790554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:50.568980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:53.379006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:56.192457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.666534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:00.912952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.647453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.291040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:06.160832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.141664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.012532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:11.892085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:45.130468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:48.056782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:50.737017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:53.569768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:56.500896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.804214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.049446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.775026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.429454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:06.596682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.277729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.147729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:12.028950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:45.257500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:48.338599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:50.951192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:53.762613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:56.740296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:58.932806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.170610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:02.905307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.552089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:06.721329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.407757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.275023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:12.161014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:45.418349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:48.522326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:51.106448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:53.996064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:56.970404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:59.061846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.310781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.019120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.680570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:06.845371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.539549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.395528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:12.308161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:45.602808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:48.757672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:51.287162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:54.201878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:57.214649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:59.190875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.448801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.136661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.803805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:06.969749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.697692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.524390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:12.445196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:45.753922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:48.964395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:51.507682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:54.351189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:57.515730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:59.339141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.583991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.260698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:04.934790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.087796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:08.877372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.654778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:12.596786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:46.005322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:49.108384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:51.750143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:54.542315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:57.690242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:59.511500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.734683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.387132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:05.070129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.219276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.014270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.793177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:12.740508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:46.234304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:49.246465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:51.919908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:54.673301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:57.839331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:36:59.684499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:01.875728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:03.508122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:05.203985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:07.341731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:09.156058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T13:37:10.919898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-14T13:37:20.798800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BrandCar_ageDrivetrainEngineFuel Tank CapacityFuel TypeHeightKilometerLengthMakeMax Power bhpMax Power rpmMax Torque NmMax Torque RpmOwnerPriceTransmissionWidthYear
Brand1.0000.2330.4320.5200.4890.2380.3540.0000.5220.6050.5920.3390.5680.3620.0770.4500.6260.5870.233
Car_age0.2331.0000.050-0.040-0.1020.359-0.1420.602-0.1040.088-0.1580.012-0.1210.1560.193-0.5200.165-0.212-1.000
Drivetrain0.4320.0501.0000.5750.5970.2890.4960.0550.4730.6260.5360.3580.5940.3630.0930.3690.4390.5240.050
Engine0.520-0.0400.5751.0000.8420.2810.2220.1210.8620.5320.879-0.5570.891-0.5170.1330.7370.5430.8370.040
Fuel Tank Capacity0.489-0.1020.5970.8421.0000.2130.2620.0780.8180.4460.839-0.5160.865-0.5480.0520.7510.5460.8720.102
Fuel Type0.2380.3590.2890.2810.2131.0000.1820.0000.2140.2560.1550.3120.2680.3000.1160.0790.2060.2560.359
Height0.354-0.1420.4960.2220.2620.1821.0000.0840.0670.5450.069-0.2900.173-0.2080.0620.1270.3220.2810.142
Kilometer0.0000.6020.0550.1210.0780.0000.0841.0000.0550.000-0.038-0.2330.076-0.0620.000-0.2790.000-0.003-0.602
Length0.522-0.1040.4730.8620.8180.2140.0670.0551.0000.4490.887-0.4480.845-0.4770.0780.7670.5720.8460.104
Make0.6050.0880.6260.5320.4460.2560.5450.0000.4491.0000.5900.4470.5230.4830.0870.5600.6780.4220.088
Max Power bhp0.592-0.1580.5360.8790.8390.1550.069-0.0380.8870.5901.000-0.4120.912-0.5220.0920.8300.6500.8740.158
Max Power rpm0.3390.0120.358-0.557-0.5160.312-0.290-0.233-0.4480.447-0.4121.000-0.6720.7360.085-0.3920.246-0.529-0.012
Max Torque Nm0.568-0.1210.5940.8910.8650.2680.1730.0760.8450.5230.912-0.6721.000-0.6920.0760.7930.5710.8930.121
Max Torque Rpm0.3620.1560.363-0.517-0.5480.300-0.208-0.062-0.4770.483-0.5220.736-0.6921.0000.104-0.5340.290-0.572-0.156
Owner0.0770.1930.0930.1330.0520.1160.0620.0000.0780.0870.0920.0850.0760.1041.0000.1090.0780.0560.193
Price0.450-0.5200.3690.7370.7510.0790.127-0.2790.7670.5600.830-0.3920.793-0.5340.1091.0000.4110.8300.520
Transmission0.6260.1650.4390.5430.5460.2060.3220.0000.5720.6780.6500.2460.5710.2900.0780.4111.0000.5780.165
Width0.587-0.2120.5240.8370.8720.2560.281-0.0030.8460.4220.874-0.5290.893-0.5720.0560.8300.5781.0000.212
Year0.233-1.0000.0500.0400.1020.3590.142-0.6020.1040.0880.158-0.0120.121-0.1560.1930.5200.1650.2121.000

Missing values

2026-02-14T13:37:13.135787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-14T13:37:13.372720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-14T13:37:13.616471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MakePriceYearKilometerFuel TypeTransmissionOwnerEngineDrivetrainLengthWidthHeightFuel Tank CapacityMax Power bhpMax Power rpmMax Torque NmMax Torque RpmCar_ageBrand
0Honda505000201787150PetrolManualFirst1198FWD3990.01680.01505.035.087.06000.0109.04500.06Budget Cars
1Maruti Suzuki450000201475000DieselManualSecond1248FWD3995.01695.01555.042.074.04000.0190.02000.09Budget Cars
2Hyundai220000201167000PetrolManualFirst1197FWD3585.01595.01550.035.079.06000.0112.04000.012Budget Cars
3Toyota799000201937500PetrolManualFirst1197FWD3995.01745.01510.037.082.06000.0113.04200.04Budget Cars
4Toyota1950000201869000DieselManualFirst2393RWD4735.01830.01795.055.0148.03400.0343.01400.05Mid-range cars
5Maruti Suzuki675000201773315PetrolManualFirst1373FWD4490.01730.01485.043.091.06000.0130.04000.06Budget Cars
6Mercedes-Benz1898999201547000PetrolAutomaticSecond1991FWD4630.01777.01432.0NaN181.05500.0300.01200.08Mid-range cars
7BMW2650000201775000DieselAutomaticSecond1995AWD4439.01821.01612.051.0188.04000.0400.01750.06Luxury cars
8Skoda1390000201756000PetrolAutomaticFirst1798FWD4670.01814.01476.050.0177.05100.0250.01250.06Mid-range cars
9Nissan575000201585000DieselManualFirst1461FWD4331.01822.01671.050.084.03750.0200.01900.08Budget Cars
MakePriceYearKilometerFuel TypeTransmissionOwnerEngineDrivetrainLengthWidthHeightFuel Tank CapacityMax Power bhpMax Power rpmMax Torque NmMax Torque RpmCar_ageBrand
2049Mercedes-Benz5950000201780000PetrolAutomaticFirst2996AWD5130.01934.01850.0100.0329.05250.0480.01600.06Luxury cars
2050Hyundai891000201647000PetrolManualFirst1591FWD4270.01780.01630.060.0122.06400.0154.04850.07Budget Cars
2051Maruti Suzuki925000202148000PetrolManualFirst1462FWD3995.01790.01640.048.0103.06000.0138.04400.02Budget Cars
2052Hyundai409999201468000DieselManualFirst1396NaN3940.01710.01505.045.090.04313.0220.02329.09Budget Cars
2053Maruti Suzuki245000201479000PetrolManualSecond1197FWD3775.01680.01620.043.085.06000.0113.04500.09Budget Cars
2054Mahindra850000201690300DieselManualFirst2179FWD4585.01890.01785.070.0138.03750.0330.01600.07Budget Cars
2055Hyundai275000201483000PetrolManualSecond814FWD3495.01550.01500.032.055.05500.075.04000.09Budget Cars
2056Ford240000201373000PetrolManualFirst1196FWD3795.01680.01427.045.070.06250.0102.04000.010Budget Cars
2057BMW4290000201860474DieselAutomaticFirst1995RWD4936.01868.01479.065.0188.04000.0400.01750.05Luxury cars
2058Mahindra670000201772000DieselManualFirst1493RWD3995.01745.01880.0NaN70.03600.0195.01400.06Budget Cars

Duplicate rows

Most frequently occurring

MakePriceYearKilometerFuel TypeTransmissionOwnerEngineDrivetrainLengthWidthHeightFuel Tank CapacityMax Power bhpMax Power rpmMax Torque NmMax Torque RpmCar_ageBrand# duplicates
0BMW2950000201584700DieselAutomaticSecond2993AWD4886.01938.01762.0NaN258.04000.0560.01500.08Luxury cars2
1BMW2975000201587000DieselAutomaticFirst2993AWD4886.01938.01762.0NaN258.04000.0560.01500.08Luxury cars2
2BMW4175000201627011DieselAutomaticSecond2993AWD4886.01938.01762.0NaN258.04000.0560.01500.07Luxury cars2
3Hyundai649000202052236PetrolManualFirst1197FWD3765.01660.01520.043.081.06000.0114.04000.03Budget Cars2
4Maruti Suzuki725000201911874PetrolAutomaticFirst1197FWD3840.01735.01530.037.082.06000.0113.04200.04Budget Cars2
5Mercedes-Benz7475000202113000DieselAutomaticFirst1950RWD5063.01860.01494.080.0192.03800.0400.01600.02Luxury cars2
6Renault315000201715507PetrolManualFirst799FWD3679.01579.01478.028.053.05678.072.04386.06Budget Cars2
7Skoda1390000201756000PetrolAutomaticFirst1798FWD4670.01814.01476.050.0177.05100.0250.01250.06Mid-range cars2